Recently, deep neural network [1]}, [2]} (DNN) models have demonstrated a competitive performance on many speech processing tasks. Nonetheless, training a large parameterized DNN using as few as 1,000 utterances usually leads to a poor speech recognition accuracy. Considering that there exist over \(8,000\)  spoken languages [3]} in the world, it is clear that some of those spoken languages may not provide enough training materials to properly deploy DNN-based spoken command recognition (SCR) systems. Meanwhile, we have witnessed a rapid growth of quantum devices, and quantum machine learning (QML) systems can now “be deployed in practices” thanks to software simulator (e.g., Tensorflow-quantum [4]}). QML-based algorithms can also be combined with modern DNN solutions to accomplish feature extraction or reduce computation complexity. In the present quantum computing era [5]} in which 4 to 200 qubits are accessible, an integration of quantum based components (e.g., feature extractor) into a classical ML module, such as quantum neural networks [6]}, [7]}, [8]} (QNNs) and quantum kernel learning [9]} (QKL), is a realistic solution that can be deployed for real-world applications. QNNs use parameterized quantum circuits (e.g., tensor network) to optimize latent features [9]} in the Hilbert space [11]} for minimizing a loss function. QNNs can be trained under gradient optimization with variational circuits [12]} in an end-to-end fashion to approximate any non-linear [13]} function.
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